{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
    "*This notebook contains an excerpt from the book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler.\n",
    "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n",
    "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n",
    "\n",
    "*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n",
    "If you find this content useful, please consider supporting the work by\n",
    "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Combining Decision Trees Into a Random Forest](10.02-Combining-Decision-Trees-Into-a-Random-Forest.ipynb) | [Contents](../README.md) | [Implementing AdaBoost](10.04-Implementing-AdaBoost.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Using Random Forests for Face Recognition\n",
    "\n",
    "A popular dataset that we haven't talked much about yet is the **Olivetti face dataset**.\n",
    "\n",
    "The Olivetti face dataset was collected in 1990 by AT&T Laboratories Cambridge. The\n",
    "dataset comprises facial images of 40 distinct subjects, taken at different times and under\n",
    "different lighting conditions. In addition, subjects varied their facial expression\n",
    "(open/closed eyes, smiling/not smiling) and their facial details (glasses/no glasses).\n",
    "\n",
    "Images were then quantized to 256 grayscale levels and stored as unsigned 8-bit integers.\n",
    "Because there are 40 distinct subjects, the dataset comes with 40 distinct target labels.\n",
    "Recognizing faces thus constitutes an example of a **multiclass classification** task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the dataset\n",
    "\n",
    "Like many other classic datasets, the Olivetti face dataset can be loaded using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading Olivetti faces from http://cs.nyu.edu/~roweis/data/olivettifaces.mat to /home/ubuntu/scikit_learn_data\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_olivetti_faces\n",
    "dataset = fetch_olivetti_faces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.data\n",
    "y = dataset.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the original images consisted of 92 x 112 pixel images, the version available\n",
    "through scikit-learn contains images downscaled to 64 x 64 pixels.\n",
    "\n",
    "To get a sense of the dataset, we can plot some example images. Let's pick eight indices\n",
    "from the dataset in a random order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(21)\n",
    "idx_rand = np.random.randint(len(X), size=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these example images using Matplotlib, but we need to make sure we reshape\n",
    "the column vectors to 64 x 64 pixel images before plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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use5iZSwbbId0iu1g30tm1D0e3HfzKPvZxShyuuscJzGP/exk6izL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mXgLAIXPyunw1/8xV9M+5hLQG3COtsVOIMl74cW+VX5LNK8D7dYmLNCLhAFZ7V4vGZR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\ni+P/ABexuNMFS/v1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8605aca828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(14, 8))\n",
    "for p, i in enumerate(idx_rand):\n",
    "    plt.subplot(2, 4, p + 1)\n",
    "    plt.imshow(X[i, :].reshape((64, 64)), cmap='gray')\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see how all the faces are taken against a dark background and are upright. The\n",
    "facial expression varies drastically from image to image, making this an interesting\n",
    "classification problem. Try not to laugh at some of them!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the dataset\n",
    "\n",
    "Before we can pass the dataset to the classifier, we need to preprocess it following the best\n",
    "practices from [Chapter 4](04.00-Representing-Data-and-Engineering-Features.ipynb), *Representing Data and Engineering Features*.\n",
    "\n",
    "Specifically, we want to make sure that all example images have the same mean grayscale\n",
    "level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples, n_features = X.shape\n",
    "X -= X.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We repeat this procedure for every image to make sure the feature values of every data\n",
    "point (that is, a row in `X`) are centered around zero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X -= X.mean(axis=1).reshape(n_samples, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The preprocessed data can be visualized using the preceding code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Z3/2Z0seZZb5iBSbG2644dh/WSbHVmRFGgJlkvs4Xuzw5r5Ry0jxjd7+6q/+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\nvOiR6sQxmjG45f/fPOgw3dHtLI4NPVG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ZsH8jbDAYDAaDwWAwGAw2MB8yg8FgMBgMBoPB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86007f89e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "for p, i in enumerate(idx_rand):\n",
    "    plt.subplot(2, 4, p + 1)\n",
    "    plt.imshow(X[i, :].reshape((64, 64)), cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.savefig('olivetti-pre.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and testing the random forest\n",
    "\n",
    "We continue to follow our best practice to split the data into training and test sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, random_state=21\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we are ready to apply a random forest to the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "rtree = cv2.ml.RTrees_create()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we want to create an ensemble with 50 decision trees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_trees = 50\n",
    "eps = 0.01\n",
    "criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,\n",
    "            num_trees, eps)\n",
    "rtree.setTermCriteria(criteria)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have a large number of categories (that is, 40), we want to make sure the\n",
    "random forest is set up to handle them accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rtree.setMaxCategories(len(np.unique(y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can play with other optional arguments, such as the number of data points required in a\n",
    "node before it can be split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rtree.setMinSampleCount(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we might not want to limit the depth of each tree. This is again, a parameter we\n",
    "will have to experiment with in the end. But for now, let's set it to a large integer value,\n",
    "making the depth effectively unconstrained:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rtree.setMaxDepth(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can fit the classifier to the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtree.train(X_train, cv2.ml.ROW_SAMPLE, y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the resulting depth of the tree using the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtree.getMaxDepth()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that although we allowed the tree to go up to depth 1000, in the end only 25\n",
    "layers were needed.\n",
    "\n",
    "The evaluation of the classifier is done once again by predicting the labels first (`y_hat`) and\n",
    "then passing them to the `accuracy_score` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, y_hat = rtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find 87% accuracy, which turns out to be much better than with a single decision tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46999999999999997"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(random_state=21, max_depth=25)\n",
    "tree.fit(X_train, y_train)\n",
    "tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad! We can play with the optional parameters to see if we get better. The most\n",
    "important one seems to be the number of trees in the forest. We can repeat the experiment\n",
    "with a forest made from 100 trees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91000000000000003"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_trees = 100\n",
    "eps = 0.01\n",
    "criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,\n",
    "            num_trees, eps)\n",
    "rtree.setTermCriteria(criteria)\n",
    "rtree.train(X_train, cv2.ml.ROW_SAMPLE, y_train);\n",
    "_, y_hat = rtree.predict(X_test)\n",
    "accuracy_score(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this configuration, we get 91% accuracy!\n",
    "\n",
    "Another interesting use case of decision tree ensembles is Adaptive Boosting or AdaBoost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Combining Decision Trees Into a Random Forest](10.02-Combining-Decision-Trees-Into-a-Random-Forest.ipynb) | [Contents](../README.md) | [Implementing AdaBoost](10.04-Implementing-AdaBoost.ipynb) >"
   ]
  }
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